Dissecting tumor cell programs through group biology estimation in clinical single-cell transcriptomics
Abstract With the growth of clinical cancer single-cell RNA sequencing studies, robust differential expression methods for case/control analyses (e.g., treatment responders vs. non-responders) using gene signatures are pivotal to nominate hypotheses for further investigation. However, many commonly...
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-57377-6 |
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| Summary: | Abstract With the growth of clinical cancer single-cell RNA sequencing studies, robust differential expression methods for case/control analyses (e.g., treatment responders vs. non-responders) using gene signatures are pivotal to nominate hypotheses for further investigation. However, many commonly used methods produce a large number of false positives, do not adequately represent the patient-specific hierarchical structure of clinical single-cell RNA sequencing data, or account for sample-driven confounders. Here, we present a nonparametric statistical method, BEANIE, for differential expression of gene signatures between clinically relevant groups that addresses these issues. We demonstrate its use in simulated and real-world clinical datasets in breast cancer, lung cancer and melanoma. BEANIE outperforms existing methods in specificity while maintaining sensitivity, as demonstrated in simulations. Overall, BEANIE provides a methodological strategy to inform biological insights into unique and shared differentially expressed gene signatures across different tumor states, with utility in single-study, meta-analysis, and cross-validation across cell types. |
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| ISSN: | 2041-1723 |